A niched Pareto genetic algorithm for multiobjective environmental/economic dispatch

By Mohammad Ali Abido. 2003

360 citations recorded by the source corpus.

A niched Pareto genetic algorithm (NPGA) based approach to solve the multiobjective environmental/economic dispatch (EED) problem is presented in this paper. The EED problem is formulated as a non-linear constrained multiobjective optimization problem. The proposed NPGA based approach handles the problem as a multiobjective problem with competing and non-commensurable cost and emission objectives. One of the main advantages of the proposed approach is that there is no restriction on the number of optimized objectives. The proposed approach has a diversity-preserving mechanism to overcome the premature convergence problem. A hierarchical clustering algorithm is developed and imposed to provide the decision maker with a representative and manageable Pareto-optimal set. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed approach are carried out on the standard IEEE 30-bus test system. The results demonstrate the capabilities of the proposed approach to generate well-distributed Pareto-optimal non-dominated solutions of the multiobjective EED problem in one single run. The comparison with the classical methods demonstrates the superiority of the proposed approach and confirms its potential to solve the multiobjective EED problem.

Subfields: Computational Theory and Mathematics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/8e921b02fb68d890f7ef346b8062ef78bef943a4

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Citations in the corpus

Multiobjective optimization techniques applied to engineering problems (2010, methodology): “The n = 6 generators, cost coefficients and emission characteristic coefficients, according to Abido (2003), are given in Tab.”
The Exact Solution of the Environmental/Economic Dispatch Problem (2012, neutral): “cations to solve the EED, [7]–[9] and [10] applied several algo-”
Economic-emission dispatch problem: A semi-definite programming approach (2014, methodology): “More recent approaches use evolutionary algorithms [14, 15] including particle swarm optimization (PSO) method [16, 17], genetic algorithms (GA) [9], bacterial foraging (BF) method [18, 19] and differential evolution [20].”
A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch (2012, methodology): “In order to compare with the results in [2–5], the compromise solution is also identified at the end of our algorithm.”